| bias_iteration_report | R Documentation |
This report is NOT an alias of bias_interaction_report() despite the
similar name. It focuses on the recalibration path of a bias run:
iteration table, convergence summary, and orientation review. Use this
to confirm that the bias recalibration itself converged; use
bias_interaction_report() to review the ranked flagged cells from
the converged run.
bias_iteration_report(
x,
diagnostics = NULL,
facet_a = NULL,
facet_b = NULL,
interaction_facets = NULL,
max_abs = 10,
omit_extreme = TRUE,
max_iter = 4,
tol = 0.001,
top_n = 10
)
x |
Output from |
diagnostics |
Optional output from |
facet_a |
First facet name (required when |
facet_b |
Second facet name (required when |
interaction_facets |
Character vector of two or more facets. |
max_abs |
Bound for absolute bias size when estimating from fit. |
omit_extreme |
Omit extreme-only elements when estimating from fit. |
max_iter |
Iteration cap for bias estimation when |
tol |
Convergence tolerance for bias estimation when |
top_n |
Maximum number of iteration rows to keep in preview-oriented summaries. The full iteration table is always returned. |
This report focuses on the recalibration path used by estimate_bias().
It provides a package-native counterpart to legacy iteration printouts by
exposing the iteration table, convergence summary, and orientation review in
one bundle.
A named list with:
table: iteration history
summary: one-row convergence summary
orientation_review: interaction-facet sign review
settings: resolved reporting options
direction_note: one-line interpretive note describing which
direction the iteration moved (carried from the bias estimator;
empty string when the underlying estimator does not emit one)
recommended_action: one-line recommended action label
(e.g. "converged", "increase max_iter"); empty string when
the underlying estimator does not emit one
estimate_bias(), bias_interaction_report(), build_fixed_reports()
toy <- load_mfrmr_data("example_bias")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 30)
diag <- diagnose_mfrm(fit, residual_pca = "none")
out <- bias_iteration_report(fit, diagnostics = diag, facet_a = "Rater", facet_b = "Criterion")
summary(out)
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